
"""simple docstring"""



import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a       :						int       		=		1_6
a       :						Dict       		=		3_2


def 			_UpperCamelCase						(    _A       ,   _A = 1_6       ,   _A = "bert-base-cased"   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    _UpperCAmelCase        = load_dataset("""glue"""       ,   """mrpc"""   )

    def tokenize_function(_A   ):
        # max_length=None => use the model max length (it's actually the default)
        _UpperCAmelCase        = tokenizer(examples["""sentence1"""]       ,   examples["""sentence2"""]       ,   truncation=_A       ,   max_length=_A   )
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    _UpperCAmelCase        = datasets.map(
        _A       ,   batched=_A       ,   remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,   load_from_cache_file=_A   )

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    _UpperCAmelCase        = tokenized_datasets.rename_column("""label"""       ,   """labels"""   )

    def collate_fn(_A   ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_A       ,   padding="""max_length"""       ,   max_length=1_2_8       ,   return_tensors="""pt"""   )
        return tokenizer.pad(_A       ,   padding="""longest"""       ,   return_tensors="""pt"""   )

    # Instantiate dataloaders.
    _UpperCAmelCase        = DataLoader(
        tokenized_datasets["""train"""]       ,   shuffle=_A       ,   collate_fn=_A       ,   batch_size=_A   )
    _UpperCAmelCase        = DataLoader(
        tokenized_datasets["""validation"""]       ,   shuffle=_A       ,   collate_fn=_A       ,   batch_size=_A   )

    return train_dataloader, eval_dataloader


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    model.eval()
    _UpperCAmelCase        = 0
    for step, batch in enumerate(_A   ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device   )
        with torch.no_grad():
            _UpperCAmelCase        = model(**_A   )
        _UpperCAmelCase        = outputs.logits.argmax(dim=-1   )
        # It is slightly faster to call this once, than multiple times
        _UpperCAmelCase     ,_UpperCAmelCase        = accelerator.gather(
            (predictions, batch["""labels"""])   )  # If we are in a multiprocess environment, the last batch has duplicates
        if accelerator.use_distributed:
            if step == len(_A   ) - 1:
                _UpperCAmelCase        = predictions[: len(eval_dataloader.dataset   ) - samples_seen]
                _UpperCAmelCase        = references[: len(eval_dataloader.dataset   ) - samples_seen]
            else:
                samples_seen += references.shape[0]
        metric.add_batch(
            predictions=_A       ,   references=_A       ,   )

    _UpperCAmelCase        = metric.compute()
    return eval_metric["accuracy"]


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _UpperCAmelCase        = config["""lr"""]
    _UpperCAmelCase        = int(config["""num_epochs"""]   )
    _UpperCAmelCase        = int(config["""seed"""]   )
    _UpperCAmelCase        = int(config["""batch_size"""]   )
    _UpperCAmelCase        = args.model_name_or_path

    set_seed(_A   )
    _UpperCAmelCase     ,_UpperCAmelCase        = get_dataloaders(_A       ,   _A       ,   _A   )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _UpperCAmelCase        = AutoModelForSequenceClassification.from_pretrained(_A       ,   return_dict=_A   )

    # Instantiate optimizer
    _UpperCAmelCase        = (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    _UpperCAmelCase        = optimizer_cls(params=model.parameters()       ,   lr=_A   )

    if accelerator.state.deepspeed_plugin is not None:
        _UpperCAmelCase        = accelerator.state.deepspeed_plugin.deepspeed_config[
            """gradient_accumulation_steps"""
        ]
    else:
        _UpperCAmelCase        = 1
    _UpperCAmelCase        = (len(_A   ) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        _UpperCAmelCase        = get_linear_schedule_with_warmup(
            optimizer=_A       ,   num_warmup_steps=0       ,   num_training_steps=_A       ,   )
    else:
        _UpperCAmelCase        = DummyScheduler(_A       ,   total_num_steps=_A       ,   warmup_num_steps=0   )

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = accelerator.prepare(
        _A       ,   _A       ,   _A       ,   _A       ,   _A   )

    # We need to keep track of how many total steps we have iterated over
    _UpperCAmelCase        = 0
    # We also need to keep track of the stating epoch so files are named properly
    _UpperCAmelCase        = 0
    _UpperCAmelCase        = evaluate.load("""glue"""       ,   """mrpc"""   )
    _UpperCAmelCase        = num_epochs

    if args.partial_train_epoch is not None:
        _UpperCAmelCase        = args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint   )
        _UpperCAmelCase        = args.resume_from_checkpoint.split("""epoch_"""   )[1]
        _UpperCAmelCase        = """"""
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        _UpperCAmelCase        = int(_A   ) + 1
        _UpperCAmelCase        = evaluation_loop(_A       ,   _A       ,   _A       ,   _A   )
        accelerator.print("""resumed checkpoint performance:"""       ,   _A   )
        accelerator.print("""resumed checkpoint's scheduler's lr:"""       ,   lr_scheduler.get_lr()[0]   )
        accelerator.print("""resumed optimizers's lr:"""       ,   optimizer.param_groups[0]["""lr"""]   )
        with open(os.path.join(args.output_dir       ,   F"""state_{starting_epoch-1}.json"""   )       ,   """r"""   ) as f:
            _UpperCAmelCase        = json.load(_A   )
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    _UpperCAmelCase        = {}
    for epoch in range(_A       ,   _A   ):
        model.train()
        for step, batch in enumerate(_A   ):
            _UpperCAmelCase        = model(**_A   )
            _UpperCAmelCase        = outputs.loss
            _UpperCAmelCase        = loss / gradient_accumulation_steps
            accelerator.backward(_A   )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        _UpperCAmelCase        = F"""epoch_{epoch}"""
        _UpperCAmelCase        = os.path.join(args.output_dir       ,   _A   )
        accelerator.save_state(_A   )
        _UpperCAmelCase        = evaluation_loop(_A       ,   _A       ,   _A       ,   _A   )
        _UpperCAmelCase        = accuracy
        _UpperCAmelCase        = lr_scheduler.get_lr()[0]
        _UpperCAmelCase        = optimizer.param_groups[0]["""lr"""]
        _UpperCAmelCase        = epoch
        _UpperCAmelCase        = overall_step
        accelerator.print(F"""epoch {epoch}:"""       ,   _A   )

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir       ,   F"""state_{epoch}.json"""   )       ,   """w"""   ) as f:
                json.dump(_A       ,   _A   )


def 			_UpperCamelCase						(    )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""   )
    parser.add_argument(
        """--model_name_or_path"""       ,   type=_A       ,   default="""bert-base-cased"""       ,   help="""Path to pretrained model or model identifier from huggingface.co/models."""       ,   required=_A       ,   )
    parser.add_argument(
        """--output_dir"""       ,   type=_A       ,   default="""."""       ,   help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""       ,   )
    parser.add_argument(
        """--resume_from_checkpoint"""       ,   type=_A       ,   default=_A       ,   help="""If the training should continue from a checkpoint folder."""       ,   )
    parser.add_argument(
        """--partial_train_epoch"""       ,   type=_A       ,   default=_A       ,   help="""If passed, the training will stop after this number of epochs."""       ,   )
    parser.add_argument(
        """--num_epochs"""       ,   type=_A       ,   default=2       ,   help="""Number of train epochs."""       ,   )
    _UpperCAmelCase        = parser.parse_args()
    _UpperCAmelCase        = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}

    training_function(_A       ,   _A   )


if __name__ == "__main__":
 main()

"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)